The smart grid is characterized as a power system that integrates real-time measurements, bi-directional communication, a two-way flow of electricity, and evolutionary computation. The power distribution system is a fundamental aspect of the electric power system in order to deliver safe, efficient, reliable, and resilient power to consumers. A distribution management system (DMS) begins with the extension of the Supervisory Control and Data Acquisition (SCADA) system through a transmission network beyond the distribution network. These transmission networks oversee the distribution of energy generated at power plants to consumers via a complex system of transformers, substations, transmission lines, and distribution lines. The major challenges that existing distribution management systems are facing, maintaining constant power loads, user profiles, centralized communication, and the malfunctioning of system equipment and monitoring huge amounts of data of millions of micro-transactions, need to be addressed. Substation feeder protection abruptly shuts down power on the whole feeder in the event of a distribution network malfunction, causing service disruption to numerous end-user clients, including industrial, hospital, commercial, and residential users. Although there are already many traditional systems with the integration of smart things at present, there are few studies of those systems reporting runtime errors during their implementation and real-time use. This paper presents the systematic model of a distribution management system comprised of substations, distribution lines, and smart meters with the integration of Internet-of-Things (IoT), Nondeterministic Finite Automata (NFA), Unified Modeling Language (UML), and formal modeling approaches. Non-deterministic finite automata are used for automating the system procedures. UML is used to represent the actors involved in the distribution management system. Formal methods from the perspective of the Vienna Development Method-Specification Language (VDM-SL) are used for modeling the system. The model will be analyzed using the facilities available in the VDM-SL toolbox.

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This study evaluates the antioxidant, antimicrobial, and antiproliferative activities of mushroom varieties (, , and) found in the Himalayan regions of Jammu and Kashmir, India. All three mushroom varieties exhibited antioxidant activity. showed the highest diphenyl-2-picrylhydrazyl inhibition activity (87.88±5.6%), showed the highest reducing power (0.49±0.01) and metal chelating ability (48.57± 0.38%), and contained the highest phenolic (5.25±0.14 mg gallic acid equivalent per gram) and flavonoid content (64.39±0.73 μg rutin equivalent per gram). showed maximum antibacterial activity against all the pathogenic strains and the highest zone of inhibition was against (7.45±0.5 mm). exhibited the highest cell proliferation inhibition against Colo-205 and MCF-7 cancer cell lines. The structural analysis by Fourier transform infrared spectroscopy indicated the presence of β-configuration in carbohydrates, phenolic compounds, and protein, which impart nutraceutical properties to mushrooms.

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Fish is primary protein source for human being in the universe. Overall 50 fish specimen of Eutropiithys vacha were collected from Indus River, Head Tounsa, Pakistan to check the effect of calcium and magnesium in the fish by Atomic Absorption Spectrometry. Calcium (Ca) and magnesium (Mg) in fish showed highly significant correlation between size (weight and length) and body burden of E. vacha. Calcium and magnesium, showed isometric relationships with body weight of wild E. vacha, indicating increase of concentrations of Ca and Mg in direct proportion to an increase in body weight of E. vacha. While Ca and Mg showed negative allometric relation in increasing fish length suggesting that these metals accumulate at a smaller proportion compared to its excretion rate as the E. vacha increases in length. Condition factor for E. vacha ranged from 0.447 to 0.930 and remained constant with metal concentrations in wet weight of the fish. The result displayed that the concentration of macroelements (Ca and Mg) in the examined fish sample were significantly affected by fish size.